Data analysis
The SPSS software for Windows (IBM SPSS Statistics 27) was used for descriptive, correlation, and regression analysis. First, HRV values that differed from the mean value by more than three standard deviations were removed. Then, we performed an overall descriptive analysis of the HRV, questionnaire scores, and demographic information (age, gender).
Pearson correlation was used to calculate the correlation coefficient between HRV, questionnaire scores, and age. Independent sample t-tests were used to examine gender differences in HRV and questionnaire scores and the effects of baseline type (i.e., resting and vanilla) on HRV. Observed power was estimated by retrospective power analysis to evaluate the statistical reliability whereas higher power indicates less probability of type II error. Then, two linear regression analyses were conducted with HRV as the dependent variable. In model 1, we included RRStotal as the independent variable and age, gender, and Depressionstandardized as control variables. In model 2, we included Brooding and Reflection together with the control variables (age, gender, and Depressionstandardized). To explore whether there is a non-linear effect between HRV and rumination or depression as reported by previous studies, we conducted curve estimations under regression analyses in SPSS with HRV as the dependent variable; RRStotal, Brooding, Reflection, the scores from BDI-II, MASQdepression, DASSdepression, and Depressionstandardized as independent variables. One independent variable was included for each estimation.